Handling detector pixel performance variation in digital positron emission tomography

ABSTRACT

A non-transitory computer-readable medium storing instructions readable and executable by a workstation (18) including at least one electronic processor (20) to perform a quality control (QC) method (100). The method includes: receiving a current QC data set acquired by a pixelated detector (14) and one or more prior QC data sets acquired by the pixelated detector; determining stability levels of detector pixels (16) of the pixelated detector over time from the current QC data set and the one or more prior QC data sets; labeling a detector pixel of the pixelated detector as dead when the stability level determined for the detector pixel is outside of a stability threshold range; and displaying, on a display device (24) operatively connected with the workstation, an identification (28) of the detector pixels labelled as dead.

CROSS-REFERENCE TO PRIOR APPLICATIONS

This application is the U.S. National Phase application under 35 U.S.C.§ 371 of International Application No. PCT/EP2018/074546, filed on Sep.12, 2018, which claims the benefit of U.S. Provisional Application No.62/561,706, filed on Sep. 22, 2017. These applications are herebyincorporated by reference herein.

FIELD

The following relates generally to the medical imaging arts, medicalimage interpretation arts, image reconstruction arts, medical imagingdevice maintenance arts, and related arts.

BACKGROUND

Digital positron emission tomography (PET) detectors include an assemblyof a large number of pixels. In one pixelated detector design, eachdetector pixel is a small scintillator crystal cut to the desired sizeand has an associated scintillation light detection unit and electronicsto detect 511 keV gamma rays in a PET scan. The crystal preparation,detector assembling process, and the like are kept as systematic aspossible so that the performance of most of the pixels are relativelysimilar and predictable, thereby forming the major subset of averagepixels.

A small portion of the detector pixels may be significantly differentthan the average pixels due to many reasons, such as crystalnon-uniformity, manufacturing processing variation, photon detectionunit (e.g., photodiode) performance fluctuation, assembly processinconsistence, electronics variation, and the like. Some of such pixelshave sensitivity much lower than the average pixels. Some of such pixelshave sensitivity much higher than the average; and still some pixels mayhave unstable sensitivity. That is, there sensitivity varies from timeto time, either dramatically or continuously.

Assessment of detector pixel performance is typically performed by wayof a detector pixel calibration involving acquisition of events datafrom a standard uniform phantom preferably placed at the scannerisocenter so as to be equidistant from the detectors of a detector ring.Due to the time and effort involved, pixel calibration is usuallyperformed infrequently, e.g. only after major maintenance or the like.The calibration typically also includes determining normalizationfactors for the detector pixels to account for differences insensitivity of individual pixels.

In some existing PET systems, pixels with extremely low sensitivity(e.g., a low limit being 20% or less of the sensitivity of the averagepixels) and pixels with artificially high sensitivity (e.g., a highlimit being 40% higher than the average pixels) are deemed as deadpixels. When pixels are identified as dead, they are excluded from thedata processing as if they don't exist. For example, the eventsassociated with dead pixels are excluded from the system performanceevaluations, such as National Electrical Manufacturer's Association(NEMA) sensitivity, count rate performance evaluation, as well as beingexcluded from the image reconstruction process (such as list-modeiterative reconstruction, for example). In reconstruction, approachesfor handling dead pixels are sometimes employed to minimize the negativeimpact of dead pixels in image quality (e.g., resolution, artifactsetc.) and quantitative accuracy (e.g., lesion intensity, SUV, etc.).

These approaches for dealing with atypical detector pixels have somedrawbacks. For instance, unstable pixels, having a sensitivityfluctuating with time between the low and high limits, can be difficultto identify. If the pixel has sensitivity varying between, for example,20% and 140% of the average pixels, the calibration of the PET systemmay not detect such pixels since the calibration is performedinfrequently and does not track performance with high temporalresolution. Normalization can implicitly compensate such variation, butany pixel performance variation occurs between two normalizations cannotbe detected and compensated.

If a group of clustered pixels (e.g., a tile, a module, and the like)have very low sensitivity (e.g., 50% of the average) or theirsensitivity is much different than other pixels, they can introducesignificant image artifacts and quantitative errors in certainsituations. Such a situation can arise if the root cause of thesensitivity variation is at the tile or module level, e.g. a problemwith the tile or module electronics that impacts all detectors of thetile or module. The normalization process can implicitly compensate suchtile- or module-level variation, but the normalization is performedinfrequently. If the sensitivity variation of the tile or module occursbetween two normalizations, then patient data may be compromised, andthe resulted images may have artifacts and quantitative errors.

Existing approaches which label pixels as dead can also beover-inclusive, so as to exclude useful imaging data. If pixels labeledas dead are stable over the duration of the scan, the counts associatedwith these dead pixels may still be useful. In fact, such counts can bevery valuable in low dose studies, short scans, or dynamic scans.However, the existing approaches exclude counts from dead pixels frombeing used.

When the number of dead pixels increases and/or they become clustered,users may get worried as to whether the system is still safe to use.Guidelines for service calls may not be sufficient for thosesophisticated users, possibly leading to unnecessary or prematuremaintenance calls. Conversely, less attentive users may fail torecognize the number of dead pixels is becoming high enough to adverselyimpact clinical imaging quality.

The following discloses new and improved systems and methods to overcomethese problems.

SUMMARY

In one disclosed aspect, a non-transitory computer-readable mediumstores instructions readable and executable by a workstation includingat least one electronic processor to perform a quality control (QC)method. The method includes: receiving a current QC data set acquired bya pixelated detector and one or more prior QC data sets acquired by thepixelated detector; determining stability levels of detector pixels ofthe pixelated detector over time from the current QC data set and theone or more prior QC data sets; labeling a detector pixel of thepixelated detector as dead when the stability level determined for thedetector pixel is outside of a stability threshold range; anddisplaying, on a display device operatively connected with theworkstation, an identification of the detector pixels labelled as dead.

In another disclosed aspect, an imaging system includes an imageacquisition device configured to acquire imaging data of a patient, theimage acquisition device including a pixelated detector with a pluralityof detector pixels, and a display device. At least one electronicprocessor is programmed to: receive a current quality control (QC) dataset acquired by the pixelated detector and one or more prior QC datasets acquired by the pixelated detector; determine stability levels ofthe detector pixels over time from the current QC data set and the oneor more prior QC data sets; label one or more of the detector pixels asdead when the stability level determined for the detector pixels isoutside of a stability threshold range; and control the display deviceto display an identification of the detector pixels labelled as dead.

In another disclosed aspect, an imaging system includes a positronemission tomography (PET) device configured to acquire imaging data of apatient. The image acquisition device includes a PET detector ring witha plurality of PET detector pixels. The imaging system also includes adisplay device. At least one electronic processor is programmed to:receive a current quality control (QC) data set acquired by thepixelated detector and one or more prior QC data sets acquired by thepixelated detector; determine sensitivity levels of the detector pixelsfrom the current QC data set; determine stability levels of the detectorpixels over time from the current QC data set and the one or more priorQC data sets; label a detector pixel of the pixelated detector as deadwhen the sensitivity level is above a maximum sensitivity threshold;label a detector pixel of the pixelated detector as cold when thesensitivity level is below a minimum sensitivity threshold; and controlthe display device to display an identification of the detector pixelslabelled as dead or cold.

One advantage resides in more effective and timely identification ofunstable detector pixels.

Another advantage resides in identifying previously unstable detectorpixels that have re-stabilized.

Another advantage resides in reconstructing imaging data including dataacquired using detector pixels with a low, but stable, sensitivitylevel.

Another advantage resides providing a clinician with an identificationof detector pixels labeled as dead.

Another advantage resides in more effectively informing a clinician ofthe effect of dead detector pixels on clinical image quality.

Another advantage resides in reducing maintenance calls for PET detectorring maintenance issues.

A given embodiment may provide none, one, two, more, or all of theforegoing advantages, and/or may provide other advantages as will becomeapparent to one of ordinary skill in the art upon reading andunderstanding the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may take form in various components and arrangements ofcomponents, and in various steps and arrangements of steps. The drawingsare only for purposes of illustrating the preferred embodiments and arenot to be construed as limiting the invention.

FIG. 1 diagrammatically shows image reconstruction system according toone aspect.

FIG. 2 shows an exemplary flow chart operation of the system of FIG. 1;and

FIG. 3 illustratively shows an identification of detector pixels on adisplay of the system of FIG. 1.

DETAILED DESCRIPTION

In existing digital PET imaging device maintenance, a detector pixelnormalization calibration is performed infrequently, e.g. after majormaintenance or on a schedule with long (e.g. multi-month) intervalsbetween calibrations. The normalization involves acquiring data from astandard uniform phantom. During the normalization calibration, anypixels with sensitivity below a minimum threshold (“cold” pixels) arelabeled as dead, and similarly any pixels with sensitivity above amaximum threshold (“hot” pixels) are also labeled as dead pixels. Therationale is that the cold pixels are most likely missing many counts,while the hot pixels are producing many spurious counts. Counts acquiredusing pixels labeled as dead pixels are ignored, and the sensitivitymatrix used in the iterative image reconstruction is adjusted to accountfor the missing pixels.

Additionally, a daily quality control (QC) procedure is typicallyperformed using a standard point source phantom, e.g. a ²²Na pointsource. The QC checks various detector pixel parameters, such as energyresolution, detector uniformity, and also detects dead pixels. Theinformation generated from the QC procedure is not used to adjust anyscanner settings, but only to verify the scanner is operating within anacceptable envelope. If, for example, the QC detects too many deadpixels, this may result in a maintenance call.

Embodiments disclosed herein advantageously leverage the existing QCprocedure to assess the day-to-day stability of the detector pixels. Insome contemplated implementations, this additional QC information isinformational, i.e. dead pixels are detected based on the sensitivitythresholds or based on instability, and if there are too many deadpixels this may trigger a maintenance call or a new normalizationcalibration.

In other embodiments disclosed herein, the information obtained from theQC data may be used to label unstable detector pixels as dead, and/or tore-label pixels currently labeled as dead to be live pixels. In oneillustrative process, the labeling includes: (1) labeling any unstablepixel as dead even if its sensitivity is within the lower/upperthreshold bounds; (2) label any pixel whose sensitivity is overthreshold as dead; and (3) label any stable pixel whose sensitivity isunder threshold as live.

In another disclosed aspect, the effect of newly identified dead pixelsmay be simulated to inform the user as to the practical clinical effectof these dead pixels. This simulation can be done in a straightforwardway: an existing phantom or clinical image is compared with the originaldataset whose reconstruction is repeated with any counts due to thenewly identified dead pixels removed (and the sensitivity matrix used inthe image reconstruction adjusted appropriately). In addition tosimulating the existing population of dead pixels, simulations arecontemplated to be done for forecasts of additional dead pixels toinform the user of when the dead pixel count becomes problematic from aclinical viewpoint. If this is done for actual clinical images, theimpact is to provide clinicians with a real-world forecast of the impactof dead pixels on clinical imaging of the type actually performed at thehospital.

In some instances, the acquired data can be saved in a performance map.For example, in the daily QC data collection, the pixel performance mapcan be obtained using coincidence counts. For existing PET systems, toavoid the geometric response variation and shadowing of the normalcrystals opposite to the real dead pixels, one can alternatively usesingles (i.e., single 511 keV gammas) of each pixel to obtain the pixelperformance map. Since the pixel performance map is obtained daily, theanalysis of pixel performance is also performed daily. The applicationof such a map in data processing can provide assurance of thetrustworthiness of the acquired data from day to day. Thus, the risk isminimized even though intra-day variance cannot be detected and handled.This day-to-day process makes daily pixel sensitivity normalizationcalibration procedure unnecessary, thereby significantly saving time andexpense (e.g., phantom, source, etc.) for daily operation.

A typical image reconstruction process implemented by existing PETsystems uses the detector normalization results to implicitly handlepixel performance variance. However, the normalization is not performeddaily as it is a time consuming process due to a need to fill a uniformphantom with activity and perform the scan and analysis. In methods andsystems disclosed herein, pixel performance variance occurring betweensuccessive normalizations is detected in a daily basis, and applying theresultant performance map in the image reconstruction can improve datafidelity and minimize the risks of data compromise. In these processes,intermittent (i.e. unstable) pixels are excluded from data processing.Any adjustment in individual pixel sensitivity that proves to be stableover time is included in a projection/backprojection model of theiterative reconstruction algorithm, or other algorithms thatmodels/includes system response in the reconstruction.

In some embodiments, a QC tool is provided for the evaluation of pixelperformance variation on system performance, image quality andquantitation. The QC tool provides loading of the pixel performance mapmeasured on a workstation system and using the map to run typicalanalysis on system performance, image quality and quantitation. The QCtool also provides for manually modifying the pixel performance map tosimulate different pixel performance variation impact scenarios fordigital PET. The QC tool can use simulated data or data acquired on thesystem for the system performance, image quality and quantitationanalysis, including count performance, resolution, contrast, noise,uniformity, SUV, etc. The QC tool provides qualitative and quantitativeresults for users to evaluate system performance against therequirements, helping users understand if the system is still suitablefor use when some large pixel performance variation is detected.

While described with reference to digital PET, the following approachesare is applicable to any type of imaging employing detectors havingdetector pixels, e.g. digital PET, transmission computed tomography(CT), digital single photon emission computed tomography (SPECT), ordigital radiography (DR) (that is, flat-panel x-ray with a flatpixelated digital x-ray detector).

With reference to FIG. 1, an illustrative medical imaging system 10 isshown. As shown in FIG. 1, the system 10 includes an image acquisitiondevice 12. In one example, the image acquisition device 12 can comprisean emission imaging device (e.g., a positron emission tomography (PET)device, a gamma camera for use in single photon emission computedtomography (SPECT), and the like); however, it will be appreciated thatany other suitable imaging modality (e.g., transmission computedtomography (CT), X-ray, and the like, as well as hybrid systems, such asPET/CT) may be used. The image acquisition device 12 includes apixelated detector 14 having a plurality of detector pixels 16 (shown asInset A in FIG. 1) arranged to collect imaging data from a patientdisposed in an examination region 17. Depending on the modality of theimage acquisition device 12, the pixelated detector 14 can be a detectorring of a PET device (e.g., an entire PET detector ring or a portionthereof, such as a detector tile, a detector module, and so forth); adetector array of a CT device; a detector of a gamma camera configuredto perform SPECT; and a digital detector array of a digital radiographydevice such as an X-ray machine.

The system 10 also includes a computer or workstation or otherelectronic data processing device 18 with typical components, such as atleast one electronic processor 20, at least one user input device (e.g.,a mouse, a keyboard, a trackball, and/or the like) 22, and a displaydevice 24. In some embodiments, the display device 24 can be a separatecomponent from the computer 18. The workstation 18 can also include oneor more databases 26 (stored in a non-transitory storage medium such asRAM or ROM, a magnetic disk, or so forth), and/or the workstation can bein electronic communication with one or more databases 27 (e.g., anelectronic medical record (EMR) database, a picture archiving andcommunication system (PACS) database, and the like). As described hereinthe database 27 is a PACS database.

The at least one electronic processor 20 is operatively connected with anon-transitory storage medium (not shown) that stores instructions whichare readable and executable by the at least one electronic processor 20to perform disclosed operations including performing a quality control(QC) method or process 100. The non-transitory storage medium may, forexample, comprise a hard disk drive, RAID, or other magnetic storagemedium; a solid state drive, flash drive, electronically erasableread-only memory (EEROM) or other electronic memory; an optical disk orother optical storage; various combinations thereof; or so forth. Insome examples, the QC method or process 100 may be performed by cloudprocessing. The QC method or process 100 is performed on a relativelyfrequent basis as compared with the detector normalization process. Forexample, in some embodiments the QC method or process 100 is performedon a daily basis, for example in the morning during startup of theimaging device. After performing the QC process 100, the imaging deviceis then used to perform clinical imaging of patients each loaded in turninto the examination region 17 for imaging and then unloaded to admitthe next clinical patient.

With reference to FIG. 2, an illustrative embodiment of the QC method100 is diagrammatically shown as a flowchart. To start the process, astandard point source phantom is loaded into the examination region 17,preferably at the isocenter of the imaging region 17. For PETcalibration, a typical standard point source phantom is a NEMA-standard²²Na point source. At 102, the at least one electronic processor 20 isprogrammed to receive a current quality control (QC) data set acquiredfrom the point source by the pixelated detector 14. The QC method 100also has access to one or more prior QC data sets acquired by thepixelated detector at earlier times, e.g. on past days. The prior QCdata sets may, for example, be stored in the database 26. The QC data(e.g., coincidence data or singles data) acquired of the point source isindicative of individual performance of the detector pixels 16, and canbe collected on a daily basis. As the point source has a standardradioactivity, e.g. measured in Bq, the counts acquired by all detectorsshould be approximately the same after considering the distance from thepoint source to the detector pixels, assuming they have identicalsensitivity. For example, in multi-ring PET detectors, a point source atan isocenter of the image acquisition device 12 leads to differentdistance to the detector pixels 16 at different pixelated detectors 14,leading to a systematic sensitivity difference. At the same time, asthere are many geometrical symmetries in the typical PET detector ringwith respect to the center of the field of view and source location,equivalently positioned detector pixels are expected to have similarsensitivity. Thus, differences in counts in the current QC data set isindicative of different pixel sensitivities. Other information aboutdetector pixel performance can also be determined. The performance dataof the detector pixels 16 can include one or more of sensitivity, energyresolution, time-of-flight (TOF) shift (i.e. detector time delay), andthe like. Instead of the mentioned ²²Na point source, in someembodiments, the current QC data and the prior QC data sets are acquiredof a reference source comprising a line radiation source from thepixelated detector 14. A line source can provide more uniformirradiation for a multi-ring PET detector. Once received, the acquiredQC data (both current and prior) can be saved in the database 26.

At 104, the at least one electronic processor 20 is programmed todetermine sensitivity and stability levels of detector pixels 16 of thepixelated detector 14 from the current QC data set and the one or moreprior QC data sets. To do so, sensitivity levels of the detector pixels16 are determined for each QC data set (e.g. the current QC data set andeach prior QC data set) based on the ratio of the actual counts acquiredby a detector pixel versus the expected counts (expected based on theradioactivity of the point or line source, and/or the average counts ofall detectors). The processor 20 determines whether the determinedsensitivity level of the detector pixels 16 is outside of a sensitivitythreshold range. A “hot” pixel may be defined as having sensitivityhigher than an upper threshold; whereas, a “cold” pixel may be definedas having sensitivity below a lower threshold. A pixel with sensitivitybetween the lower and upper thresholds is deemed to be a normal pixel.

Detector pixel stability is determined as the change over time of thesensitivity of the detector pixel in the current and prior QC data sets.Since a QC data set is typically acquired on a daily basis, it isstraightforward to determine the stability, that is, the sensitivity asa function of time, with a “per day” temporal interval. An unstabledetector pixel is one whose sensitivity variation over time isunacceptably large. For example, detector pixels 16 are identifiedhaving a large sensitivity variation as a function of time (e.g., oneday the sensitivity is 10% of the average, and another day 60% of theaverage, and so forth). It should be noted that a detector pixel couldbe classified as “normal” in the sense that its sensitivity asdetermined from the current QC data set is between the lower and upperthresholds, and yet be classified as “unstable” if its sensitivityvaries significantly from day to day as determined from the past andcurrent QC data sets (even if this day-to-day variation remains withinthe lower and upper thresholds).

At 106, the at least one electronic processor 20 is programmed to labelat least one of the detector pixels 16 of the pixelated detector 14 as“dead” when the sensitivity level determined for the detector pixel isoutside of a sensitivity threshold range (e.g., having a lower limit ofless than 20% of the average sensitivity and having a higher limit ofgreater than 140% of the average sensitivity), and the remainingdetector pixels are labeled as “live”. For example, the “hot” detectorpixels 16 is labeled as dead when the determined sensitivity level isabove a maximum sensitivity threshold of the sensitivity thresholdrange. The “cold” detector pixels 16 are labeled as dead when thedetermined sensitivity levels is below a minimum sensitivity thresholdof the sensitivity threshold range. In a further example, one or moredetector pixels 16 previously labeled as dead can be re-labeled as livewhen the detected stability level of the one or more detector pixelspreviously labeled as dead is within the stability threshold range.

Furthermore, in the operation 106 the at least one electronic processor20 is programmed to label at least one of the detector pixels 16 of thepixelated detector 14 as “dead” when the stability level determined forthe detector pixel is outside of a stability threshold range. Forexample, the stability threshold range may be a change in sensitivity ofno more than 20% over the past five days, as a non-limiting illustrativeexample. Using this example, if a detector pixel has measuredsensitivity for the past five days (including the current day) of: 70%;65%; 60%; 62%; 72% then this pixel is deemed to satisfy the 20%stability threshold range. By contrast, a detector pixel having measuredsensitivity for the past five days (including the current day) of: 52%,70%; 75%; 79%; 71% would be deemed to be instable since its sensitivityhas ranged between 52% and 79% (a range of 27%, larger than the 20%stability threshold range). This pixel would be labeled as dead due toits instability, even though the measured sensitivities all fall wellwithin the sensitivity threshold range (20%-140%). Alternatively, theremay some second pass statistical steps that can be taken beforedetermining it is dead; for example, single outliers in the QC data maybe excluded, and/or a second pass assessment may include increasing thenumber of QC data points used, considering the statistical variance, acombination thereof, or so forth.

In the previous example, a pixel is labeled as dead if it fails eitherthe sensitivity threshold range or the stability threshold range. Inanother embodiment, the stability assessment is used to retain somestable pixels which would ordinarily be labeled as dead. In one suchexample, the cold detector pixels 16 are labeled as live, and includedin further data processing operations, if they satisfy the stabilitythreshold range. By contrast, in this example hot pixels are labeled asdead even if they are stable. The rationale for this approach is thatcold pixels which are stable are still providing useful counts ofradiation detection events; whereas, hot pixels which are stable areunreliable since the high sensitivity is likely due a high dark countrate which is unrelated to radiation detection events.

Retention of cold, but stable, detector pixels has substantialadvantages, especially in the case of low counts imaging. For example,for point source scans or patient scans with very small foci, if thecounts from such cold pixels are excluded, then pixel filling approachesmay be taken to estimate the counts associated with such pixels. Thepixel filling approaches, however, may introduce error/bias to the dataif the filling is using the average or extrapolation of the neighboringpixels. This is especially true if the pixels are clustered, for whichthe spatial resolution loss will be also significant. Using the countsfrom the cold (but stable) detector pixels 16 with the correspondingperformance map preserves accuracy of the data (including spatialresolution) and overcome a slightly higher noise level associated withthe cold pixels.

The output of the detector pixel assessment at 104, 106 operating on thecurrent and prior QC data sets may be variously used, as described next.

At 108, in one application the at least one electronic processor 20 isprogrammed to control the display device 22 to display an identification28 of the detector pixels 16 labelled as dead. The identification can bedisplayed in any suitable format, such as a list, a detector map, andthe like. In some examples, the identification 28 can display a map ofthe detector 14 with pixels labeled as dead marked using filled boxesand live pixels marked using unfilled boxes. A listing of the deadpixels may additionally or alternatively be provided. In someembodiments, the map may differently mark those pixels labeled as deaddue to being outside of the sensitivity threshold range versus thosepixels labeled as dead due to being outside of the stability thresholdrange. Alternatively, two different detector maps may be shown (for outof sensitivity range and for unstable pixels). While this type ofpresentation may be useful, it has a potential disadvantage in that theclinician may have difficulty understanding how the mapped dead pixelspractically impacts the clinical images. Due to this uncertainty as tothe practical clinical effect of dead pixels, it is possible that theclinician may order a service call when it is not (yet) needed.Conversely, if the clinician underestimates the practical clinicaleffect of dead pixels then the imaging system may continue to be usedfor clinical imaging when it would be preferable to service the detector14.

At 110, in another application, the effect of dead pixels on actualclinical images is presented. To this end, the at least one electronicprocessor 20 is programmed to reconstruct acquired or simulated imagingdata with and without the dead pixels, so as to demonstrate the impactof the dead pixels in the clinical image space. In one example, theprocessor 20 is programmed to simulate imaging data that would beacquired for an imaging subject by the pixelated detector 14, e.g. usingsimulation. Two image reconstructions are performed. The first isperformed using all simulated imaging data (referred to here as the“reference image”). The reference image can be generated from data inthe prior QC data sets, including data acquired with previously labeleddead pixels, image reconstructed by omitting previously labeled deadpixels, and the image quality and quantitation have been deemed assatisfactory. The second is performed using all simulated imaging dataexcept with the detector pixels 16 labeled as dead treated as providingno data (referred to here as the “current QC image”). The data simulatedto have been acquired by these dead pixels are excluded from furtherdata processing, and the reconstructed process is adjusted based onthese excluded pixels. The resulting current QC image and referenceimage are presented side-by-side on the display or in some other easilyperceived comparative format (e.g. toggling between the two images inresponse to the user pressing a button) so that the clinician can seethe impact of the dead pixels on a clinical image).

For a more advanced user, the foregoing simulation might instead beperformed for a point or line phantom, with the simulated data againbeing reconstructed to form current QC and reference images. Thesimplified geometry of the point or line phantom may provide moreinformative comparative images for a user with greater understanding ofthe PET imaging data acquisition and image reconstruction process.

It should be noted that the simulation of the imaging data can beperformed once, and stored in the database 26 of the workstation 18.Likewise, the reference image reconstruction using all imaging data maybe done once and stored in the database 26 of the workstation 18.Thereafter, performing the operation 110 merely entails performing thecurrent QC image reconstruction using the stored simulation data withthe current set of dead pixels omitted.

The presentation(s) at 108, 110 may be passively presented to the user.In another approach, the QC process 100 may additionally perform activediagnostics on these results to provide maintenance recommendations.Thus, at 112, the at least one electronic processor 20 is programmed todetermine whether maintenance or calibration of the pixelated detector14 is indicated based on the detector pixels 16 labeled as dead. Forexample, if a large number of detector pixels 16 are labeled as dead,then the pixelated detector 14 may need to be re-calibrated. If, afterthe calibration process, a large number of detector pixels 16 are stilllabeled as dead, then a maintenance order can be requested to replacethe pixelated detector 14. In this instance, the display device 20displays a message indicating a recommendation of performing maintenanceof the pixelated detector 14 or a recommendation of performing acalibration of the pixelated detector 14 in accord with thedetermination of whether maintenance or calibration of the pixelateddetector 14 is indicated. This operation allows users to systematicallyevaluate the impact of the dead detector pixels 16, including systemcount performance, resolution, image quality and quantitation in NEMAstudies, patient studies, and the like.

In another example, the processor 20 is programmed to perform therecommendation operation 112 using the reconstructed images produced at110. Based on the comparison of the current QC image and the referenceimage, the processor 20 determines whether maintenance or calibration ofthe pixelated detector 14 is indicated as being needed. In thisinstance, the display device 20 displays a message indicating arecommendation of performing maintenance of the pixelated detector 14 ora recommendation of performing a calibration of the pixelated detector14 in accord with the determination of whether maintenance orcalibration of the pixelated detector 14 is indicated.

FIG. 3 shows an example of the display device 20 displaying anindication of the identification 28 of the detector pixels 16. Theidentification 28 shows a sensitivity vs. time relationship betweendifferent classifications of detectors pixels 16. A first curve 1 showsa sensitivity of a detector pixel 16 is above the maximum sensitivitythreshold. In one embodiment, this pixel is labeled as “dead” becauseall hot pixels are labeled as “dead” regardless of stability under therationale that the high sensitivity is likely due a high dark count ratewhich is unrelated to radiation detection events. A second curve 2 showsa detector pixel 16 in which the performance thereof stabilizes towithin the acceptable sensitivity threshold range after the originalcalibration problem. A third curve 3 shows a detector pixel 16 that iswithin the sensitivity threshold range, but the sensitivity thereofvaries significantly at different times. If this pixel is thusdetermined to be unstable, then it is labeled as “dead” even though itssensitivity is within the acceptable range. A fourth curve 4 shows adetector pixel 16 that is below the minimum sensitivity threshold limit(i.e. “cold”), but the performance thereof is stable. This stabledetector pixel 16 may also be used in the reconstruction operations,under the rationale that cold pixels which are stable are stillproviding useful counts of radiation detection events. A fifth curve 5shows a detector pixel 16 with a sensitivity that decreasessignificantly. This detector pixel 16 is identified as at risk, and cancontinue to fail after another calibration process. As this pixelexhibits incipient instability due to its continually decreasingsensitivity, and eventually decreases below the lower threshold(transitions to a “cold” pixel) it is ultimately relabeled as “dead”. Inthis case, a warning is triggered for preventive check/maintenancebefore the scheduled maintenance, thereby improving system reliability.The service may include replacing modules, or, where dead pixels areoccurring in clusters, the detector tiles or modules may be rearrangedto break up these clusters and improve overall performance.

The disclosure has been described with reference to the preferredembodiments. Modifications and alterations may occur to others uponreading and understanding the preceding detailed description. It isintended that the invention be construed as including all suchmodifications and alterations insofar as they come within the scope ofthe appended claims or the equivalents thereof.

The invention claimed is:
 1. A non-transitory computer-readable mediumstoring instructions readable and executable by a workstation includingat least one electronic processor to perform a quality control (QC)method, the method comprising: receiving a current QC data set acquiredby a pixelated detector and one or more prior QC data sets acquired bythe pixelated detector; determining stability levels of detector pixelsof the pixelated detector over time from the current QC data set and theone or more prior QC data sets, wherein a stability level of thedetector pixel is determined based on a change of a sensitivity of thedetector pixel between the current QC data set and the one or more priorQC data sets; labeling a detector pixel of the pixelated detector asdead when the stability level determined for the detector pixel isoutside of a stability threshold range; and displaying, on a displaydevice operatively connected with the workstation, an identification ofthe detector pixels labelled as dead.
 2. The non-transitorycomputer-readable medium of claim 1, wherein the method furtherincludes: determining sensitivity levels of the detector pixels of thepixelated detector from the current QC data set; and labeling a detectorpixel of the pixelated detector as dead when the sensitivity level isabove a maximum sensitivity threshold.
 3. The non-transitorycomputer-readable medium of claim 2, wherein the method furtherincludes: labeling a detector pixel of the pixelated detector as coldwhen the sensitivity level is below a minimum sensitivity threshold andhas a stable sensitivity level over time; wherein the display alsoincludes identification of the detector pixels labeled as cold.
 4. Thenon-transitory computer-readable medium of claim 2, wherein the methodfurther includes: labeling a detector pixel of the pixelated detector asdead when the sensitivity level is below a minimum sensitivitythreshold.
 5. The non-transitory computer-readable medium of claim 1,wherein the method further includes: determining whether maintenance orcalibration of the pixelated detector is indicated based on the detectorpixels labeled as dead; wherein the display on the display devicefurther includes a message indicating a recommendation of performingmaintenance of the pixelated detector or a recommendation of performinga calibration of the pixelated detector in accord with the determinationof whether maintenance or calibration of the pixelated detector isindicated, the message further indicating a recommendation to rearrangea cluster of detector pixels labeled as dead in the pixelated detector.6. The non-transitory computer-readable medium of claim 1, wherein theoperation of labeling a detector pixel of the pixelated detector as deadwhen the stability level determined for the detector pixel is outside ofa stability threshold range further includes: re-labeling one or moredetector pixels previously labeled as dead as live when the detectedstability level of the one or more detector pixels previously labeled asdead is within the stability threshold range.
 7. The non-transitorycomputer-readable medium of claim 1, wherein the method furtherincludes: simulating imaging data acquired for an imaging subject by thepixelated detector with the detector pixels labeled as dead treated asproviding no data; and reconstructing the simulated imaging data togenerate a simulated image.
 8. The non-transitory computer-readablemedium of claim 1, wherein the method further includes: reconstructingan imaging data set acquired by the pixelated detector with dataacquired by the detector pixels labeled as dead omitted to generate acurrent QC image; and displaying, on the display device a comparison ofthe current QC image with a reference image generated with the one ormore prior QC data sets.
 9. The non-transitory computer-readable mediumof claim 8, wherein the method further includes: comparing the currentQC image and the reference image; determining whether maintenance orcalibration of the pixelated detector is indicated based on thecomparison of the current QC image and the reference image; wherein thedisplay on the display device further includes a message indicating arecommendation of performing maintenance of the pixelated detector or arecommendation of performing a calibration of the pixelated detector inaccord with the determination of whether maintenance or calibration ofthe pixelated detector is indicated.
 10. An imaging system, comprising:an image acquisition device configured to acquire imaging data of apatient, the image acquisition device including a pixelated detectorwith a plurality of detector pixels; a display device; and at least oneelectronic processor programmed to: receive a current quality control(QC) data set acquired by the pixelated detector and one or more priorQC data sets acquired by the pixelated detector; determine stabilitylevels of the detector pixels over time from the current QC data set andthe one or more prior QC data sets, wherein a stability level of thedetector pixel is determined based on a change of a sensitivity of thedetector pixel between the current QC data set and the one or more priorQC data sets; label one or more of the detector pixels as dead when thestability level determined for the detector pixels is outside of astability threshold range; and control the display device to display anidentification of the detector pixels labelled as dead.
 11. The imagingsystem of claim 10, wherein the at least one electronic processor isfurther programmed to: determine sensitivity levels of the detectorpixels of the pixelated detector from the current QC data set; label adetector pixel of the pixelated detector as dead when the sensitivitylevel is above a maximum sensitivity threshold; label a detector pixelof the pixelated detector as cold when the sensitivity level is below aminimum sensitivity threshold control the display device to display theidentification of the pixels as dead or cold.
 12. The imaging system ofclaim 10, wherein the at least one electronic processor is furtherprogrammed to: determine whether maintenance or calibration of thepixelated detector is indicated based on the detector pixels labeled asdead; wherein the display on the display device further includes amessage indicating a recommendation of performing maintenance of thepixelated detector or a recommendation of performing a calibration ofthe pixelated detector in accord with the determination of whethermaintenance or calibration of the pixelated detector is indicated. 13.The imaging system of claim 10, wherein the at least one electronicprocessor is further programmed to: re-label one or more detector pixelspreviously labeled as dead as live when the detected stability level ofthe one or more detector pixels previously labeled as dead is within thestability threshold range.
 14. The imaging system of claim 10, whereinthe at least one electronic processor is further programmed to: simulateimaging data acquired for an imaging subject by the pixelated detectorwith the detector pixels labeled as dead treated as providing no data;and reconstruct the simulated imaging data to generate a simulatedimage.
 15. The imaging system of claim 10, wherein the at least oneelectronic processor is further programmed to: reconstruct an imagingdata set acquired by the pixelated detector with data acquired by thedetector pixels labeled as dead omitted to generate a current QC image;and control the display device to display a comparison of the current QCimage with a reference image generated from the one or more prior QCdata sets.
 16. The imaging system of claim 15, wherein the at least oneelectronic processor is further programmed to: compare the current QCimage and the reference image; determine whether maintenance orcalibration of the pixelated detector is indicated based on thecomparison of the current QC image and the reference image; wherein thedisplay on the display device further includes a message indicating arecommendation of performing maintenance of the pixelated detector or arecommendation of performing a calibration of the pixelated detector inaccord with the determination of whether maintenance or calibration ofthe pixelated detector is indicated.
 17. The imaging system of claim 10,wherein the pixelated detector is one of: a detector ring of a positionemission tomography (PET) device; a detector array of a transmissioncomputed tomography (CT) device; a detector of a gamma camera configuredto perform single photon emission computed tomography (SPECT); and adigital detector array of a digital radiography device.
 18. The imagingsystem of claim 10, wherein the current and one or more prior QC datasets are acquired of a reference source comprising a point or lineradiation source.
 19. An imaging system, comprising: a positron emissiontomography (PET) device configured to acquire imaging data of a patient,the image acquisition device including a PET detector ring with aplurality of PET detector pixels; a display device; and at least oneelectronic processor programmed to: receive a current quality control(QC) data set acquired by the pixelated detector and one or more priorQC data sets acquired by the pixelated detector; determine sensitivitylevels of the detector pixels from the current QC data set; determinestability levels of the detector pixels over time from the current QCdata set and the one or more prior QC data sets, wherein a stabilitylevel of the detector pixel is determined based on a change of asensitivity of the detector pixel between the current QC data set andthe one or more prior QC data sets; label a detector pixel of thepixelated detector as dead when the sensitivity level is above a maximumsensitivity threshold; label a detector pixel of the pixelated detectoras cold when the sensitivity level is below a minimum sensitivitythreshold; and control the display device to display an identificationof the detector pixels labelled as dead or cold.
 20. The imaging systemof claim 19, wherein the at least one electronic processor is furtherprogrammed to: re-label one or more detector pixels previously labeledas dead as live when the detected stability level of the one or moredetector pixels previously labeled as dead is within the maximum andminimum stability thresholds.